SolarGest: Ubiquitous and Battery-free Gesture Recognition using Solar Cells
This work addresses the need for low-power, ubiquitous gesture recognition in battery-free devices, representing a novel application rather than an incremental improvement.
The researchers tackled the problem of enabling gesture recognition on solar-powered devices by analyzing photocurrent patterns, achieving 96% detection accuracy with transparent solar cells while reducing power consumption by 44% compared to light sensor systems.
We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems.